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Рис. 3. Teratocephalon hexahamus gen. n., sp. n.: A — трофико-сенсорный отΑеΛ теΛа; B — трофико-генитаΛьный отΑеΛ теΛа; C, D — переΑний конец теΛа. a — анус, am — амфиΑы, v — вуΛьва, cc — гоΛовная капсуΛа, ve — «жеΛуΑочек», pu — заΑняя матка, nr — нервное коΛьцо, rc — прямая кишка, au — переΑняя матка, r — ренетта, ep — экскреторная пора, o — яичник, e — яйцо Fig. 3. Teratocephalon hexahamus gen. n., sp. n.: A — trophic-sensory part of the body; B — trophic-reproductive part of the body; C, D — anterior end of the body. a — anus; am — amphids; v — vulva; cc — cephalic capsule; ve — "ventricle"; pu — posterior uterus; nr — nerve ring; pu — anterior uterus; r — renetta; rc — rectum; ep — excretory pore; o — ovary; e — egg in Recent data on soil nematodes of the families Teratocephalidae and Metateratocephalidae from Primorsky Region, Russia
Рис. 3. Teratocephalon hexahamus gen. n., sp. n.: A — трофико-сенсорный отΑеΛ теΛа; B — трофико-генитаΛьный отΑеΛ теΛа; C, D — переΑний конец теΛа. a — анус, am — амфиΑы, v — вуΛьва, cc — гоΛовная капсуΛа, ve — «жеΛуΑочек», pu — заΑняя матка, nr — нервное коΛьцо, rc — прямая кишка, au — переΑняя матка, r — ренетта, ep — экскреторная пора, o — яичник, e — яйцо Fig. 3. Teratocephalon hexahamus gen. n., sp. n.: A — trophic-sensory part of the body; B — trophic-reproductive part of the body; C, D — anterior end of the body. a — anus; am — amphids; v — vulva; cc — cephalic capsule; ve — "ventricle"; pu — posterior uterus; nr — nerve ring; pu — anterior uterus; r — renetta; rc — rectum; ep — excretory pore; o — ovary; e — egg
Рис. 1. Teratocephalus lirellus Andrassy, 1969: A — трофико-сенсорный отΑеΛ теΛа; B — поΛовая система; C — фрагмент теΛа с боковым поΛем; D — хвост; E — фрагмент поΛовой системы и среΑней кишки; F, G — переΑний конец теΛа. am — амфиΑы, lf — боковое поΛе, v — вуΛьва, cc — гоΛовная капсуΛа, pu — заΑняя матка, au — переΑняя матка, r — ренетта, ep — экскреторная пора, o — яичник Fig. 1. Teratocephalus lirellus Andrassy, 1969: A — trophic-sensory part of the body; B — reproductive system; C — fragment of the body with a lateral field; D — tail; E — fragment reproductive system and intestine; F, H — anterior end of the body. am — amphid; lf — lateral field; v — vulva; cc — cephalic capsule; pu — posterior uterus; au — anterior uterus; r — renetta; ep — excretory pore; o — ovary in Recent data on soil nematodes of the families Teratocephalidae and Metateratocephalidae from Primorsky Region, Russia
Рис. 1. Teratocephalus lirellus Andrassy, 1969: A — трофико-сенсорный отΑеΛ теΛа; B — поΛовая система; C — фрагмент теΛа с боковым поΛем; D — хвост; E — фрагмент поΛовой системы и среΑней кишки; F, G — переΑний конец теΛа. am — амфиΑы, lf — боковое поΛе, v — вуΛьва, cc — гоΛовная капсуΛа, pu — заΑняя матка, au — переΑняя матка, r — ренетта, ep — экскреторная пора, o — яичник Fig. 1. Teratocephalus lirellus Andrassy, 1969: A — trophic-sensory part of the body; B — reproductive system; C — fragment of the body with a lateral field; D — tail; E — fragment reproductive system and intestine; F, H — anterior end of the body. am — amphid; lf — lateral field; v — vulva; cc — cephalic capsule; pu — posterior uterus; au — anterior uterus; r — renetta; ep — excretory pore; o — ovary
Рис. 2. Euteratocephalus montanus sp. n.: A — трофико-сенсорый и трофико-генитаΛьный отΑеΛы теΛа; B — хвост; C — трофико-генитаΛьный отΑеΛ теΛа; D — фрагмент теΛа с боковым поΛем; E, F — переΑний конец теΛа. am — амфиΑы, lf — боковое поΛе, v — вуΛьва, va — вагина, g — гемизониΑ, cc — гоΛовная капсуΛа, ve — «жеΛуΑочек», pr — преректум,r — ренетта, f — фазмиΑа,ep — экскреторная пора, o — яичник, e — яйцо Fig. 2. Euteratocephalus montanus sp. n.: A — trophic-sensory and trophic-reproductive parts of the body; B — tail; C — trophic-reproductive part of the body; D — fragment of the body with a side field; E, F — anterior end of the body. am — amphid, lf — lateral field; v — vulva; va — vagina; g — gemizonid; cc — cephalic capsule; ve — "ventricle"; pr — prerectum; r — renetta; f — phasmids, ep — excretory pore; o — ovary; e — egg in Recent data on soil nematodes of the families Teratocephalidae and Metateratocephalidae from Primorsky Region, Russia
Рис. 2. Euteratocephalus montanus sp. n.: A — трофико-сенсорый и трофико-генитаΛьный отΑеΛы теΛа; B — хвост; C — трофико-генитаΛьный отΑеΛ теΛа; D — фрагмент теΛа с боковым поΛем; E, F — переΑний конец теΛа. am — амфиΑы, lf — боковое поΛе, v — вуΛьва, va — вагина, g — гемизониΑ, cc — гоΛовная капсуΛа, ve — «жеΛуΑочек», pr — преректум,r — ренетта, f — фазмиΑа,ep — экскреторная пора, o — яичник, e — яйцо Fig. 2. Euteratocephalus montanus sp. n.: A — trophic-sensory and trophic-reproductive parts of the body; B — tail; C — trophic-reproductive part of the body; D — fragment of the body with a side field; E, F — anterior end of the body. am — amphid, lf — lateral field; v — vulva; va — vagina; g — gemizonid; cc — cephalic capsule; ve — "ventricle"; pr — prerectum; r — renetta; f — phasmids, ep — excretory pore; o — ovary; e — egg
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov. in A Review of the Wasp Genus Tachytes Panzer, 1806 of Madagascar (Hymenoptera: Crabronidae)
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov.
Data and R scripts for the paper "New Evidence for a Directed Forgetting Effect in Source Memory and a Role of Source Feature Intrinsicality in the Item-Method"
<p>Data and R scripts from Experiment 1 and 2 of the paper "New Evidence for a Directed Forgetting Effect in Source Memory and a Role of Source Feature Intrinsicality in the Item-Method".</p>
Taxon sampling and inferred community phylogenies: R replication code and data.
<p>1 ) Code for simulating community phylogenies:</p> <p>community_simulations_creation.R</p> <p>[taxon].gene</p> <p>[taxon].phy</p> <p>[taxon].RAxML_bestTree.tre</p> <p>[taxon].Simulate.A.Community.pl</p> <p>[taxon].Simulate.B.Community.pl</p> <p>[taxon].Simulate.C.Community.pl</p> <p>[taxon].Simulate.D.Community.pl</p> <p> </p> <p>2) R code for creating and comparing phylogenetic diversity metrics:</p> <p>simulated_metric_calculation_and_comparison.R</p> <p>empirical_metric_calculation_and_comparison.R</p> <p> </p> <p>3) R code and data for statistical analyses:</p> <p>simulated_data_analysis.R</p> <p>empirical_data_analysis.R</p> <p>simulated_interval_individual_lme_data.csv</p> <p>simulated_summary_interval_individual_lme_data.csv</p> <p>empirical_interval_individual_lme_data.csv</p> <p>empirical_summary_interval_lme_data.csv</p> <p> </p>
Questionnaire, R Scripts and Response Data Set of the Survey on Functionally Similar Code Clones
<p>In 2017, we conducted an open online survey regarding functionally similar code clones with practitioners. We make the used questionnaire, the data from the response to the questionnaire and our used R script for the analysis openly available.</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 5. Hierarchical clustering by scores across the EPQ–R scales for data about all the participants
<p>The clusters were generated using an implementation of a hierarchical clustering algorithm available in the R environment (R, n.d.). The top three clusters were extracted from a hierarchical cluster tree shown in Figure 5, while the color of data points in the visualization shown in figure 4 was determined based on cluster labels. Hierarchical clusters could be used when investigating which students in the analyzed sample share similar personality traits. This could be especially useful for smaller student groups as the teacher may manually inspect the cluster tree and its leaves, which designate individual students. For instance, there are three students in cluster 3, who are represented within the tree in Figure 5 by identifiers 14, 22, and 24. The students with identifiers 14 and 22 are more closely linked and more similar to each other than to the student with identifier 24. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
R replication code and data for: Do modern hunter-gatherers live in marginal habitats?
<p>Data and R replication code for testing the Marginal Habitat Hypothesis. The R code files contain all models and are organized by the figures they generate for the associated paper.</p> <p>The data are sourced from:</p> <p>1) the Standard Cross Cultural Sample (SCCS).</p> <p>2) NASA Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data (MOD17A3 algorithm) from Numerical Terra Dynamic Simulation Group at the University of Montana.</p> <p>3) Marine Ecoregions Of the World (MEOW): <a href="http://maps.tnc.org/files/metadata/MEOW.xml">http://maps.tnc.org/files/metadata/MEOW.xml</a></p> <p>4) Terrestrial Ecoregions Of the World (TEOW): <a href="http://maps.tnc.org/files/metadata/TerrEcos.xml">http://maps.tnc.org/files/metadata/TerrEcos.xml</a></p>
Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'
<p>One raw data file and one R script that reproduces all analyses and figures reported in the paper '<strong>Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study</strong>' by Nettle et al. </p>
Data and R-script to analyse timing of northward migration of Sanderlings (Calidris alba) through Europe
<p>The data file contains data of observations of individual Sanderlings (<em>Calidris alba</em>) from specific wintering areas observed during migration in different latitude sectors through Europe. This is a subset of the raw data file which also includes observations at the wintering grounds. Each individual is indicated with a unique number in column “Individual”. The column “Winter Area” indicates the winter location of each individual in sectors of 5 degrees latitude (as depicted in Fig. 1 in the manuscript), with 7 indicating Scotland, 8 England, 9 France, 10 North Iberia, 11 Portugal, 13 Canary Islands, 15 Mauritania, 18 Ghana and 23 Namibia, as indicated in the column “Winter_Country”. “Date_DOY” indicates the day of year on which current observation was made. “Year” indicates the year during which an observation was made, “lat” is the latitude and “lon” the longitude of each observation in decimal degrees. “Lat_Sector” specifies in which of the sectors of 6 latitudinal degrees in Europe (see Fig. S1) the observation was made. “Sex” indicates the sex, based on molecular methods of an individual where 0=unknown, 1=female, 2=male. “Age”is the age of an individual, where 50=juvenile (i.e. less than 1 year old), 99=adult (i.e more than 1 year old) and 0 indicates that the age was unknown. “migration” indicates whether an observed individual is conisdered to be on migration (“yes” i.e. seen at least 2 latitudinal degrees north of its average winter location) or not (“no”). Further details can be found in the methods section in the manuscript.</p> <p>The R-script uses this dataset to analyse the variation in timing of migration through Europe for individual Sanderlings from different non-breeding areas. Comments and explanations can also be found in the script and in the methods section of the manuscript.</p> <p> </p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities
<p>This is the first release of the data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities.</p>
R data objects for the HumanDEU package
<p>This submission contains several R data objects that are part of the R package HumanDEU available through Github (https://github.com/areyesq89/HumanTissuesDEU). The objects correspond to processed data needed to reproduce the statistics, tables and figures presented in the manuscript:<br> <br> A Reyes and W Huber. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic Acids Research, 2017. doi: https://www.doi.org/10.1093/nar/gkx1165<br> <br> For more details, please visit the Github repository.</p>
Supporting Data for "Refractive index matched, nearly hard polymer colloids" (Proc. R. Soc. A, doi:10.1098/rspa.2018.0763)
<p>SAXS data [Q / Å^{-1}, I(Q) / Arb. unit, error I(Q) / Arb. unit] as *.dat files</p> <p>Data for Figures 1, 2, and 5 [description and units in column headers] as *.csv files</p>
Data and R-code on a cross-sectional study of factors associated with lameness in dairy cows housed in freestall and compost-bedded pack dairy farms in southern Brazil
<p>The data correspond to a cross-sectional study designed to investigate factors associated with lameness in dairy cows on intensive farms in southern Brazil.<br> Farms: 38 freestall and 12 compost-bedded pack visited once in 2016. All lactating cows (n = 13,716) were examined and body condition score (BCS) and gait score were assessed. Additionally, some variables were collected through inspection of facilities and using data from an interview with farmers on routine herd management practices.<br> Additional information is provided in the published paper ("Factors associated with lameness prevalence in lactating cows housed in freestall and compost-bedded pack dairy farms in southern Brazil" https://doi.org/10.1016/j.prevetmed.2019.104773)</p>
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Time-to-fatigue data for five Cypriniformes fish species and R script for data analysis
<p>The Excel file contains data from fixed velocity fatigue experiments for five small-sized Cypriniformes fish species. The recorded data includes common and scientific names of fish species, date and time of test trial, test flume length [cm], flow velocity treatment [cm/s], time-to-fatigue [sec], test water temperature [°C], fish mass [g], fish fork length [cm], fish width [cm], and fish height [cm]. The readme text file explains the column names used in the Excel file. The Rscript file contains the code used to analyse the data.</p>
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.